KR20070115964A - Systems, methods, and computer programs for non-binary sequence comparisons - Google Patents

Abstract

Systems and methods for performing non-binary comparisons of biological sequences according to the present invention include a new measure, Cω ₀ , which is a non-binary count measure used in a stand alone module called VaSSA-I. The measures yield substantially more information about sequences and comparisons between them than are gathered by conventional bioinformatics techniques.

Description

System, method and computer program for non-binary sequence comparison {SYSTEM, METHOD AND COMPUTER PROGRAM FOR NON-BINARY SEQUENCE COMPARISON}

This application claims priority based on US Provisional Application 60 / 662,943, filed March 18, 2005. The entirety of this provisional application is incorporated herein by reference.

The present invention generally relates to bioinformatics, and more particularly to methods for measuring the degree of similarity and difference between gene sequences.

DNA sequences of whole genomes of different species are rapidly determined. It is the duty of the bioinformatics community to understand the structural variations and functions of these genomes. In addition, some completed versions of genomic data contain gaps from which data cannot be obtained. These various genomic sequence data groups may consist of data whose relative order and orientation are difficult to determine. Dealing with such incomplete data places new demands on integrated system tools, especially when two or more genomes are compared. The bioinformatics community should be able to handle the gap more effectively.

In conventional methods, handling the comparison between genomes is a major problem. For extremely similar sequences, there is a so-called "greedy" alignment method for calculating the optimal alignment. The algorithm allows for whitespace in the sort and is very efficient, but only works well for very simple sort scoring schemes. For richer scores (which are involved in comparing large stretches of a single genome and multiple genomes), these excessive methods lose their efficiency, which is superior to dynamic programming.

Conventional alignment methods for three or more sequences are almost entirely suitable for comparison of protein sequences based on putative codons, which is a set of three nucleic acid bases encoding a single amino acid. This may be due to the fact that there are only a few examples of genomic sequence data from several similar species. In addition, sequence comparisons and homology analysis are on a two-way basis. It conserves computer resources but ignores biochemical information.

There is a need for improved solutions that overcome the disadvantages of conventional sequence alignment similarities and genetic sequence comparison tools.

<Overview of invention>

The system for sequencing comprises an analysis module adapted to calculate a non-membered similarity score between the first nucleotide sequence and the second nucleotide sequence; File management module; And a plot module.

In one embodiment, the system further includes a report module, a user option module and / or a user support module.

In another embodiment, the file management module comprises a sequence load module adapted to load at least one sequence file; an active sequence flush module adapted to flush the sequence file from memory; and A loaded sequence flush module adapted for flushing the loaded sequence file from memory.

In another embodiment, the sequence loading module includes a loaded sequence display module adapted to generate and display a summary report notebook page when the sequence is loaded, wherein the summary report notebook page displays the sequence file name and the number of sequences. To be adopted.

In other embodiments, the report module is adapted to generate and display sequence summaries, a list of the content of each loaded sequence, and / or statistical information for each loaded sequence.

In another embodiment, the analysis module comprises a sequence alignment module adapted to align the target sequence to the base sequence and display the alignment report; Calculated for ω ₀ score for a sequence and adapted to display the score ω ₀ ω ₀ module; A query repeat module adapted to locate multiple occurrences of the target sequence within the base sequence and display the multiple occurrences; A question omega repeat module adopted to determine when the repeated nucleotides are duplicates; A gradient calculation module adapted to calculate the slope for each nucleotide position within the base sequence and display the slope report; And a sequence comparison module adapted to compare the target sequence to the base sequence and display the similarity report.

In another embodiment, the plot module comprises a spectral array module adapted to plot alignment coefficients against base and target sequences; Single stranded modules adapted for plotting single strands against base and target sequences; Calculating a slope for each nucleotide position in the nucleotide sequence, and calculating the ω _N for the tilt module, and a nucleotide sequence adapted to display a plot of the slope and the ω _N module adapted to display a plot of ω _N Include.

Another aspect of the invention relates to a method for sequence analysis. The method reads a sequence file; Selecting a target sequence and base sequence from the file; A non-membered comparison is performed between the target and the base sequence, where the non-membered comparison produces a comparison value; Determining similarity between the target and the base sequence based on the comparison value.

In one embodiment, the method further comprises recording the aligned sequence in a sequence file and calculating the alignment ratio.

In another embodiment, the method further comprises generating at least one of a two-dimensional spectral array plot or a two-dimensional single strand plot.

In another embodiment, performing the non-binary comparison comprises using a look-up table that includes non-binary similarity score values for a plurality of possible comparisons between two sequence components. Include.

These and other features and advantages of the present invention will next become apparent from the description of preferred embodiments of the present invention, more particularly as illustrated in the accompanying drawings, in which like reference numerals are generally the same and / or functionally similar. And / or structurally similar components.

1 shows a flowchart of an exemplary method according to the present invention.

2 illustrates an exemplary embodiment of a submodule of a DNA analysis module according to the present invention.

3 illustrates an exemplary embodiment of a GUI main window in a Variation Sequence Software Application (hereinafter "VaSSA").

4 illustrates an example embodiment of a file menu window in VaSSA.

5 illustrates an example embodiment of a notebook viewer (VIEWER) window in VaSSA.

6 illustrates an exemplary embodiment of a Sequence Summary Report window in VaSSA.

7 illustrates an exemplary embodiment of a sequence view report window in VaSSA.

8 illustrates an exemplary embodiment of a sequence view STAT window in VaSSA.

9 illustrates an exemplary embodiment of a sequence alignment menu window in VaSSA.

10 illustrates an exemplary embodiment of a sequence report window aligned in VaSSA.

11 illustrates an example embodiment of a question repeat window in VaSSA.

12 illustrates an example embodiment of a question repeat report window in VaSSA.

FIG. 13 illustrates an exemplary embodiment of an Omega SUBZERO window in VaSSA.

14 illustrates an exemplary embodiment of an omega subzero report window in VaSSA.

15 illustrates an example embodiment of a question omega repeat menu window in VaSSA.

FIG. 16 illustrates an example embodiment of a question omega repeat report in VaSSA.

17 illustrates an example embodiment of a slope calculation window in VaSSA.

18 illustrates an example embodiment of a tilt calculation report in VaSSA.

19 depicts an exemplary embodiment of a sequence comparison window in VaSSA.

20 illustrates an exemplary embodiment of a sequence comparison report window in VaSSA.

21 illustrates an exemplary embodiment of a spectral array window in VaSSA.

22 illustrates an example embodiment of a spectral array plot window in VaSSA.

23 is a photograph of a spectral array formula (FORMULA).

24 shows a schematic diagram of an example spectrum array equation.

25 is a photograph of a spectral array triangular structure.

FIG. 26 illustrates an exemplary embodiment of a single stranded window in VaSSA.

FIG. 27 is an exemplary embodiment of a single stranded plot report window in VaSSA comparing two 360 base sequences (top) and a spectral array plot of the region (base) of position 250 to position 295 of the sequence at single base resolution It is shown.

FIG. 28 depicts an exemplary embodiment of an additional single strand plot report window in VaSSA, showing a comparison between single strand sequences.

29 illustrates an example embodiment of a plot slope window in VaSSA.

30 shows the slope plot for a single sequence.

31 illustrates an example embodiment of an omega SUBN window in VaSSA.

32 illustrates an exemplary embodiment of an omega SUBN plot window in VaSSA.

Figure 33 shows the chemical structure of uracil replacing thymine in RNA with four bases of nucleic acid guanine, cytosine, adenine and thymine.

34A shows photographs of the different components involved in A＼G comparison.

34B shows photographs of the different components involved in the G＼A comparison.

34C shows photographs of the different components involved in the A＼C comparison.

35 illustrates an exemplary embodiment of a DNA topological conjugacy module according to the present invention.

Figure 36 illustrates an exemplary embodiment of a DNA approximation module in accordance with the present invention.

Figure 37 illustrates an exemplary embodiment of a DNA orbit module in accordance with the present invention.

Figure 38 illustrates an exemplary embodiment of a Chaotic Region Classification Module in accordance with the present invention.

Figure 39 illustrates an exemplary embodiment of a DNA bifurcation module in accordance with the present invention.

40 depicts an exemplary embodiment of a DNA derivative module according to the present invention.

Figure 41 illustrates an exemplary embodiment of a DNA analytical behavior profiler module in accordance with the present invention.

42 illustrates an exemplary embodiment of a structural stable region module in accordance with the present invention.

Figure 43 illustrates an exemplary embodiment of a non-degradable region module in accordance with the present invention.

Figure 44 illustrates an exemplary embodiment of a DNA complexity base module according to the present invention.

45 illustrates an exemplary embodiment of a DNA aligner module according to the present invention.

46 depicts an exemplary embodiment of a non-binary sequence comparison system in accordance with the present invention.

Embodiments of the present invention provide an integrated system for analyzing and determining the structural behavior of sequences on separate phase spaces. The technology provides, in particular, new and improved measurable methods, including standardization, compression techniques, structure classification and phase conjugation methods. Combinations of these analytical methods take into account computational mathematical techniques, biology, and chemistry to generate numerical governing properties and / or structural behavior patterns of genomic data.

The present invention can be used in a variety of bioinformatics applications. The integrated systems and methods of the present invention provide a single sequence plot and other data for nucleotide sequences of essentially any length (eg, 50 to 2 million bases). The integrated systems and methods of the present invention can provide comparative data for multiple sequences due to efficient processing steps. For example, the system has been shown to operate very fast on 500 sequences of 500 bases. Comparison of 1000, 10,000, 100,000, 1,000,000, or more sequences is within the scope of the present invention.

The system of the present invention utilizes a non-binary method of generating meaningful comparison information within a homology range of 0% (no identity) to 100% (complete identity). The non-binary methods of the present invention are much more discriminating than typical binary comparisons and can resolve the degree of sequence differences that are indistinguishable from binary comparisons.

The systems and methods of the present invention are effective in comparing sequences despite the presence of insertions or deletions of any length. The sort module provides both global and local optimizations to allow meaningful comparisons. Single stranded plots and comparisons can be generated in the coding (degradable) region and non-coding (non-degradable) region with chaotic sequence or omega repeats.

DNA bases (A, T, G, and C) are used in the description below. However, it should be understood that the systems and methods of the present invention are applicable not only to DNA but to all nucleotides including RNA (uracil instead of thymine), LNA, PNA, and other synthetic nucleotide variants.

The display shown in the figure generally depicts only nucleotide sequences. As will be apparent, for the coding region, amino acid sequences corresponding to the codons can also be displayed using conventional techniques well known to those skilled in the art.

The method of the present invention includes analyzing, retrieving and displaying genomic information. The systems and methods of the present invention include the collection, storage, analysis, retrieval, data mining and data visualization and display of genomic, proteomic, and medical data; Sequence alignment and pattern recognition; And tools for structural prediction. For example, the systems and methods of the present invention can be used for predictive biochemical models in the design of silicon analysis, distributed computing, diagnostics, and treatment plans.

The system of the present invention consists of one or more modules. The modules and systems of the present invention may be implemented by stand-alone computers operating individually, or as part of a distributed computing "system" operated by several entities. The invention also encompasses various aspects of the system, such as hardware, software, subsystems, components of the subsystem, and structures of data created, edited, or collected using the system. The invention also includes analytical instrumentation associated with methods and apparatus for gathering, generating and displaying relevant data and methods for operating and using the instrument. It is also contemplated to sell sales methods using the systems and methods of the present invention, such as subscriptions for sequence analysis tools.

Embodiments of the embodiments described in more detail below will use conventional methods of biology, microbiology, molecular biology and immunology, unless otherwise indicated in the art. Such techniques are explained fully in the literature. All publications, patents, and patent applications cited herein above or below are hereby incorporated by reference in their entirety.

Justice

In describing the present invention, the following terms will be used and are intended to be defined as indicated below.

"VaSSA" stands for Variation Sequence Software Application.

"Computer" means any device capable of receiving a structured input signal, processing the structured input signal in accordance with a predetermined rule, and generating a result of the processing as an output signal. A computer may include any device that accepts data, processes the data according to one or more stored software programs, and produces a result, for example, and typically includes input, output, storage, arithmetic, logic, And a control unit. Examples of computers include computers; General purpose computer; Supercomputer; Mainframe; Super mini-computer; Mini-computer; Workstation; Micro-computers; server; Interactive television; Web equipment; A communication device with internet access; Hybrid combination of computer and interactive television; Portable computer; Personal digital assistant (PDA); Cell Phone; And use-specific hardware and / or software to mimic a computer, such as a programmable gate array (PGA) or a programmed digital signal processor (DSP). The computer may be stationary or portable. A computer can have a single processor or multiple processors that can operate in parallel and / or non-parallel. A computer also refers to two or more computers connected together over a network to send or receive information between the computers. Examples of such computers include distributed computer systems for processing information through computers connected by a network.

"Machine-accessible media" means any storage device used to store data accessible by a computer. Examples of machine-readable media include magnetic hard disks; Floppy disks; Optical discs such as CD-ROMs and DVDs; Magnetic tape; Memory chips; And carrier waves for carrying machine-readable electronic data, such as those used in e-mail transmission and reception or network access.

"Software" means certain rules for operating a computer. Examples of software include software; Code segment; indication; Software programs; Computer program; And programmed logic.

"Computer system" means a system having a computer that includes a machine-readable medium that implements software for operating the computer.

"Information storage device" means an article of manufacture used for storing information. Information storage devices come in different forms, for example paper and electronic forms. In paper form, the information storage device comprises paper printed with information. In electronic form, the information storage device comprises a machine-readable medium for storing information as software, for example as data.

The following terms are not standard terms in genetics and bioinformatics.

A "string" is a sequence of characters. The sequence can be regarded as an n x 1 matrix known as the n-pair of subjects (letters). In the case of nucleotide sequences, such as DNA, RNA, or synthetic or other variants, each nucleotide component has a unique position in the string that is a separate set.

Example: AGCAATATAGGA is a string of 12 characters in length.

"Subsequence" of string S means a sequence of letters of S that need not be contiguous within S but maintain the same order as given in S.

Example: ACG is a subsequence of ACTCGT.

"f (n) = O (g (n))": f (n) and g (n) are functions. Then f (n) = O (g (n)) only when the constant c is present, so that | f (n) | ≤ cg (n) which is sufficiently large for all n.

“S ₄ ” is a set of DNA sequences on four nucleotides: A, C, G, and T.

_{σ L: S 4 -> S} 4, only _{σ k, L (S 0 S} 1 S 2 ··· S n ···) = s 0 s 1 s 2 ··· s n ··· ( here, k = 1 (indicating to move to 1), and L to shift from left to right. Thus, σ _L is a continuous DNA valued function defined on S ₄ . One way to visualize a map is simply to “forget” the first input in the sequence and focus all other inputs to the right (ie, the underlined portion of the sequence). The intuitive concept of DNA continuity can be explained by stating that asymptotic language variation on the small neighbor of any positional DNA subsequence in S ₄ will only slightly change from that position. The variation can be made small or large as desired by decreasing or increasing the size of the neighbor.

σ _{t, R} is the analog map for the above, which moves to the left in units of t and reads from the right. The continuity of these maps allows the maps to be combined.

Forward and backward orbits of subsequences: The forward orbits of subsequence z are sets of points z, σ _L (z), σ ² _L (z), σ ³ _L (z), ... , O ⁺ (z). Orbit backward of the lower sequence z is a point _{z, σ R (z),} σ 2 R (z), σ 3 R (z), and a set of ···, O ^- is denoted by (z).

Fixed and periodic sub-sequences: DNA sub-sequences is s is σ _L (s) = s is a fixed sub-sequence for the σ _L. The DNA subsequence s is the periodic subsequence of period n if σ ⁿ _L (s) = s. The minimum amount of n is called the first period of s. The set of all iterations of the periodic point forms a periodic orbit.

Ultimate periodicity: If s is not periodic but m> 0 exists such that σ ^{n + i} _L (s) = σ ⁱ (s) for all ⁱ ≧ m, then DNA subsequence s is the ultimate periodicity of period n. That is, σ ⁱ _L (s) is periodic for i≥m.

이면, 하위서열 x는 s에 전향 점근성이다. S^s(s)로 표시되는 s의 안정한 세트는 s에 전향 점근성인 모든 하위서열로 이루어진다.Forward asymptotes: Let s be the DNA subsequence that is the periodicity of period n.

If subsequence x is forward asymptotic on s. The stable set of s, denoted by S ^s (s), consists of all subsequences that are forward asymptotic to s.

"Sorter" is one version of multiple sequence alignment analysis.

"Omega Comparator" is a single and multiple sequence base search base on the ω ₀ measure.

A "spectral array" is a series of calculations that allows comparing all the nucleotides in the multiple strings that produce their unique structure in terms of the ω ₀ measure that allows to find the optimal language behavior.

The "DNA ω ₀ Gene Code Viewer" is a more sophisticated classification of genetic codes with measures ω ₀ .

A "stable analytical profiler" is a technique that defines a set of all subsequences that are forward asymptotic to the target subsequence.

An "unstable analytical profiler" is a technique that defines a set of all subsequences that are retrospective asymptotic to a target subsequence.

Chaos: (1) σ _L (z) has a sensitive dependency on target subsequences; (2) σ _L (z) is phase shifted; (3) If the periodic subsequence is dense with respect to the string or data set, then σ _L (z) is said to be chaotic.

A "symbolic DNA orbit" is an iterative process that is the asymptotic sign behavior of a target subsequence within a sequence.

An “analytical DNA orbit” is the asymptotic language behavior of a target subsequence in a sequence.

"DNA Approximation Analysis" is a set of techniques that provides accurate structural behavior at low complexity subsequences.

"Chaotic region classification" uniquely divides subsequence targets into three categories: (1) targets that are sensitive to initial conditions, (2) topologically transitional targets, and (3) dense periodic subsequences in their DNA sequences. It is a technique to do.

"DNA induction" is a measure that allows qualitative observation of a change from one nucleotide to the next in a DNA sequence.

"DNA branching" is a technique of observing changes in subsequences under different parameters.

"DNA phase conjugate" is a technique that shows when different mappings of σ _L (z) are completely equivalent.

으로 정의되고, 여기서, s_i/t_i는 비-2원 함수이고, 그의 예는 표 1 및 2에 정의되고, N은 비교되는 두 서열 중 더 짧은 서열 내의 뉴클레오티드의 수이다. 오메가 유사성 스코어는 룩업 테이블에 주어진 비교의 값을 갖는, 염기 위치 i에서 임의의 2개의 뉴클레오티드 스트링, s 및 t의 비-2원 비교이다.A "trust score" is a measure of classifying a family of sequences from the closest to the furthest to the target sequence. Omega similarity score, or ω ₀ measure

Wherein s _i / t _i is a non-binary function, examples of which are defined in Tables 1 and 2, and N is the number of nucleotides in the shorter of the two sequences being compared. The omega similarity score is a non-binary comparison of any two nucleotide strings, s and t, at base position i with the value of the comparison given in the lookup table.

Embodiments of the present invention are discussed in detail below. While certain exemplary embodiments are discussed, it should be understood that this is for illustrative purposes only. Those skilled in the art will appreciate that other components and configurations may be used without departing from the spirit and scope of the invention.

1 is an exemplary embodiment. The method 100 of the present invention reads the sequence file (101); Selecting a target sequence and a nucleotide sequence from the file (103); Comparing the target sequence to base sequence (s) using a non-two-way comparison (105); Generate a similarity score (107); Recording the aligned sequence to a file (109). Optionally, the method 100 generates a visual representation of the comparison (111), calculates an alignment ratio; The method may further comprise generating a two-dimensional single stranded plot or spectral array plot 113, a multi stranded report 115, or another plot 117.

The sequence file may be a machine-readable file comprising one or more gene sequences. There are various acceptable formats for DNA sequences. EMBL format is acceptable. In this format the sequence file may comprise a plurality of sequences. One sequence entry is initiated using an identifier line (“ID”) followed by an additional comment line. The initiation of a sequence can be labeled by a line beginning with "SQ" and the end of the sequence can be labeled by two diagonal lines ("//"). FASTA format is also acceptable. Sequences in the FASTA format begin with a single-line description followed by a line of sequence data. The description line must begin with a large inequality (">") symbol in the first column. Many other formats may also be acceptable, such as GCG, GenBank, and IG.

The sequence data may be in text form, for example ASCII, or some other representation readable by a computer executing the method of the present invention. Reading the sequence file may include typing the sequence directly, reading from disk, or accessing the public domain using a well known interface such as Entrez. The file can be saved and analyzed, or analyzed "on the fly." The user can choose to read a single file or multiple files, or an entire database, or a file or multiple files, or any subsequence of any length within the entire database.

The target is a subsequence of any length. The user can choose to perform the analysis on a database or on a file so that the structural behavior can be observed. The targets are distinguished from each other in two stages. The first biological link is the alphabet that makes up the subsequence target. The second linkage is an omega zero biological linkage.

In one embodiment, generating the spectral array plot calculates ω _N ; Perform a radial comparison; Extract the alignment coefficients; Plotting the alignment coefficients.

In another embodiment, generating the spectral array plot includes reversing either the base or the target; Inverting the mod further.

In another embodiment, performing the non-binary comparison includes using a lookup table that includes non-binary similarity score values for a plurality of possible comparisons between two sequence components.

In another embodiment, the methods of the present invention compare the molecular structure of the first nucleotide to the second nucleotide; Determine a first non-binary similarity score based on the comparison; Describe the similarity score for each nucleotide in a lookup table; Calculating a second non-binary similarity score comparing the target sequence (t) of the nucleotides to the base sequence (s) of the nucleotides using a lookup table.

46 illustrates an embodiment of a non-binary sequence comparison system 10 of the present invention. System 10 includes an analysis module 200, a file management module 300, a plot module 400 and optionally, reports adapted to calculate a non-binary similarity score between a first nucleotide sequence and a second nucleotide sequence. Module 500, user option module 600, and / or user support module 700.

File management module 300 of non-binary sequence comparison system 10 manages sequence files. In one embodiment, file management module 300 includes sequence loading module 310 adapted for loading at least one sequence file; An active sequence flush module 320 adapted to flush the sequence file from memory; And a loaded sequence flush module 330 adapted to flush the loaded sequence file from memory. In another embodiment, sequence loading module 310 includes a loaded sequence display module 312 adapted to generate and display a summary report notebook page when a sequence is loaded. The summary report notebook page is adapted to display the sequence file name and the number of sequences.

In another embodiment, the plot module 400 of the non-binary sequence comparison system 10 includes a spectral array module 410 adapted to plot alignment coefficients against base and target sequences; A single strand module 420 adapted to plot the single strand against the base sequence and the target sequence; Gradient module 430 adopted to calculate the slope for each nucleotide position in the base sequence and display a plot of the slope, and ω adopted to calculate ω _N for the base sequence and display a plot of ω _{N N} module 440. In a preferred embodiment, the spectral array module 410 is further employed to calculate ω _N values for radial comparison and to extract alignment coefficients. In another preferred embodiment, the single stranded module 420 is adapted to calculate ω _N values for the base sequence and the target sequence.

In another embodiment, the report module 500 of the non-binary sequence comparison system 10 of the present invention provides a sequence summary, a list of the contents of each loaded sequence, and / or statistical information about each loaded sequence. It is adopted to create and display.

In another embodiment, the analysis module 200 of the non-binary sequence comparison system 10 includes a sequence alignment module 201 adapted to align the target sequence to the base sequence and display an alignment report; A ω ₀ module 203 adopted to calculate a ω ₀ score for the sequence and display the ω ₀ score; A query repeat module 205 adopted to locate multiple occurrences of the target sequence within the base sequence and display the multiple occurrences; A query omega repeat module 207 employed to determine when the repeated nucleotides are duplicates; Gradient calculation module 209, adapted to calculate the slope for each nucleotide position within the base sequence and display the slope report; And a sequence comparison module 211 adopted for comparing the target sequence to the base sequence and displaying the similarity report.

In a preferred embodiment, sequence alignment module 201 reverses the base sequence, reverses the mode, and / or aligns the base and target in the shortest length, and / or calculates the alignment ratio. It is further employed to perform the action of calculating the omega similarity score.

In another preferred embodiment, the sequence comparison module 211 reverses the base sequence, reverses the target sequence, reverses the mode, calculates ω _N values for each base and target sequence, and calculates the base and target sequence. It is further employed to convert to binary, calculate the distance between the base sequence and the target sequence, and perform the action of determining if the distance exceeds the bound.

FIG. 2 shows the layout of the desired module disassembly of the DNA analysis portion of the VaSSA architecture. Modules in disassembly are discussed in more detail below. The submodules are shown in flow chart form in FIGS. 35 to 45.

VaSSA Architectural  Module dismantling

DNA Analysis Module Group 200

  SSDA (Single Strand DNA Analysis) Module Group 210

  MSDA (Multi-Strand DNA Analysis) Module Group 240

SSDA (Single Strand DNA Analysis) (Figure 2)

  DNA Approximation Module 212

  Chaotic Area Classification Module 214

  DNA Induction Module 216

  DNA Branch Module 218

  DNA Orbit Module 220

  Analytical Behavior Profiler Module 222

  DNA Phase Conjugation Module 224

  Structural Stability Area Modules 226

  Non-Resolvable Area Modules 228

  DNA Complexity Base Module 230

  DNA Sorter Module 232

MSDA (Multi Strand DNA Analysis) (FIG. 2)

  DNA Approximation Module 242

  Chaos Region Classification Module 244

  DNA Induction Module 246

  DNA Branch Module 248

  DNA Orbit Module 250

  Analytical Behavior Profiler Module 252

  DNA Phase Conjugation Module 254

  Structural Stability Area Module 256

  Non-Resolvable Area Module 258

  DNA Complexity Base Module 260

  DNA Sorter Module 262

DNA phase conjugated modules 224 and 254 (FIG. 35)

  a. Analytical Profiler Module 3501

  b. Analytic Mapper Module (Creating Analytic Mappings) 3503

  c. Conjugation Comparison Module 3505

  d. First Iterative Analysis Module 3507

  e. Phase Portrait Generator Module 3511

DNA Approximation Modules 212 and 242 (FIG. 36)

  a. Holomorphic Shape Generator Module 3601

  b. Approximate Constructor Module 3603

  c. P & Q Coefficient Calculator Module 3605

  d. JC-DNA Curve Generator Module 3607

  e. Low Complexity Generator Module 3609

  f. Target Classifier Module 3611

  g. Symbolic DNA Orbit Module (also Child of SSDA and MSDA) 3613

  h. Analytical DNA Orbit Module (also Child of SSA and MSDA) 3615

DNA Orbits 220 and 250 (Analytical DNA Orbit Module, FIG. 37)

  Symbol DNA Orbit Module 3701

    a. Symbolic Flow Generator Module 3703

    b. Low Difference Generator Module 3705

    c. Orbit Generator Module 3707

  Analytical DNA Orbit Module 3709

    a. Analytical Forward Profiler Module 3711

    b. Analytical Backward Profiler Module 3713

    c. DNA Attractor Generator Module 3715

    d. DNA Repeller Generator Module 3717

Chaotic Region Classification Modules 214 and 244 (FIG. 38)

  Chaos Area Sorter 3801

    a. DNA Sensitivity Generator Module 3803

    b. DNA Transitivity Generator Module 3805

    c. Compact Periodic Sequence Generator Module 3807

DNA branching modules 218 and 248 (FIG. 39)

  Splitter Sorter 3901

    a. DNA Transition Splitter Profiler Module 3903

    b. DNA Dense Splitter Profiler Module 3905

DNA Induction Modules 216 and 246 (FIG. 40)

  Induction Generator Module 4001

  Monotonic Generator Module 4003

    a. Positive Measure Module 4005

    b. Negative Measure Module 4007

Analytical Behavior Profiler Modules 222 and 252 (FIG. 41)

  DNA Approximation Module 4101

  Chaos Zones Classification Module 4103

  DNA Induction Module 4105

  DNA Branch Module 4107

  DNA Orbit Module 4109

  Analytical Behavior Profiler Module 4111

  DNA Phase Conjugation Module 4113

  Structural Stability Area Module 4115

  Non-Resolvable Area Module 4117

  DNA Complexity Base Module 4119

  DNA Sorter Module 4121

Algebraic Structure Generator Module 4123

  a. Group Generator Module 4125

  b. Semi-Group Generator Module 4127

  c. Ring Generator Module 4129

  d. Analytic Set Generator Module 4131

Homomorphism-Generator Module 4133

Isomorphism-Generator Module 4135

Structural Stability Area Modules 226 and 256 (Figure 42)

  Repeat Generator Module 4201

  Forward Asymptotic Module 4203

  Reliability Profiler Module 4205

Non-Resolvable Area Modules 228 and 258 (FIG. 43)

  DNA Orbit Analysis Module 4301

  Non-Repeat Generator Module 4303

  Non-degradable Profiler Module 4305

DNA Complexity Base Modules 230 and 260 (FIG. 44)

  Repeat Generator Module 4401

  Universal DNA Base Generator Module 4403

  Density Generator Module 4405

DNA Sorter Modules 232 and 262 (Figure 45)

  Symbol sorter module 4501

    a. Single Strand Generator Module 4503

    b. Multi-Single Strand Generator Module 4505

  Omega Compare Sorter Module 4507

    a. Omega Single Strand Generator Module 4509

    b. Multi-Single Strand Generator Module 4511

VaSSA Description of the main module

DNA Approximation Module 212 or 242 : The module reduces the polynomial configuration in VaSSA. It shows that not all coefficients of f are required to perform the calculation. The approximation also produces data that can be used for visualization of the language structure behavior of low complexity subsequences. The procedure is performed without losing any biological information. Approximations exist in a less order that provides faster and more accurate analysis, and the calculation provides better fitting of the original function.

Chaotic Region Classification Module 214 or 244 : The module has three components: unpredictability, regularity, and elements that cannot be broken down into smaller subsequences.

DNA Derivation Module 216 or 246 : The module creates an environment in which monotone changes in the content can be observed when the DNA string is read from left to right and / or from right to left. When DNA induction is positive, the information conveyed increases. When DNA induction is negative, the information conveyed decreases. When DNA induction is zero, the information conveyed is constant.

DNA Branch Module 218 or 248 : The module analyzes changes in the DNA map when undergoing parameter changes. Such changes often include periodic subsequences of DNA, but other changes also.

DNA Orbit module 220 or 250 : Analysis of DNA sequences is mathematical in nature, but the module creates an environment that answers rather non-mathematical questions, such as where subsequences go and what they do there. The module incorporates a geometric process of taking one subsequence to another, assuming that the DNA sequences are separate sets.

Analytical Behavior Profiler Module 222 or 252 : The module considers all its child modules and links them through an algebraic function method that does not lose biological content. This further improves the information by subdividing the dynamic information from the child module into algebraic equivalent classes.

DNA Phase Conjugation Module 224 or 254 : The module associates a data set to a data set, a DNA sequence to a DNA sequence, and multiple DNA sequences to a DNA sequence. This creates an environment that classifies sequences that are completely equivalent and unequal.

Structural Stability Area Module 226 or 256 : The module is concerned with understanding all orbits and identifying sets of orbits such as periodic and ultimate periodic asymptotes. Implementation of qualitative and / or geometric techniques to understand a given data set.

Non-resolvable zone module 228 or 258: the module for all non-related to identifying a set of Orbit-understand Orbit, non non-periodic, eventually periodic points or spirit. Implementation of qualitative and / or geometric techniques to understand a given data set.

DNA Complexity Base Module 230 or 260 : The module produces a universal DNA set that allows observation of the way in which the non-periodic subsequence is arbitrarily close to another sequence. The module creates a matching environment in a place where the language behavior is very high, which creates a verbally dense orbit. The orbit is called phase shiftability.

DNA Aligner module 232 or 262: the module is a version of the system of VaSSA tool kit for analyzing the sequence alignment. In addition, the module can be enhanced using additional biological information modules such as symbolic DNA orbits and the like.

3 through 28 illustrate exemplary embodiments of a graphical user interface (GUI) with VaSSA during VaSSA execution.

The aligned sequence can then be written back to the sequence file, or to a different file. The ratio of alignments can then be calculated, which shows the ratio of two sequences present in the alignment.

로서 정의된다. 오메가 유사성 스코어, 또는 ω₀ 측도는 임의의 2개의 뉴클레오티드 스트링, s 및 t의 비-2원 비교이다. 이는 상기 등식에서 s_i/t_i를 s_i/s_{i +1}로 치환함으로써 단일 스트링에 대한 분석을 위해 쉽게 변형될 수 있다.Omega similarity score (ω ₀ ) can also be calculated. The algebraic structure of ω ₀

It is defined as The omega similarity score, or ω ₀ measure, is a non-binary comparison of any two nucleotide strings, s and t. This can be easily modified for analysis on a single string by replacing s _i / t _i with s _i / s _{i +1} in the equation.

Omega similarity scores can be calculated in several ways. The value of the s _i / t _i comparison is based on the chemical structure of the nucleotides of the DNA. There are four possible bases in the DNA: adenine (A), cytosine (C), guanine (G) and thymine (T). In RNA, thymine is replaced by uracil (U). The structure of this base is shown in FIG. The purines adenine and guanine have two ring structures, and the pyrimidines cytosine, thymine and uracil have a single ring structure. The values s _i / t _i represent the difference in structure between the various bases. There are two rings within the purine base structure, which can be considered large 6-membered rings and small 5-membered rings. The pyrimidine structure has only one ring. Measurements can be classified into four categories: purine＼purine, pyrimidine＼pyrimidine, purine＼pyrimidine, and pyrimidine＼purine.

Traditional methods of comparing DNA sequences work by comparing base sequences in a binary fashion, ie simply by evaluating whether the bases are identical or different. In one aspect, the present invention relates to a method of comparing DNA sequences that not only considers different bases but also measures the magnitude of the differences. Accordingly, the present invention includes non-binary methods of comparing DNA sequences.

In the first embodiment, three-dimensional problems are mainly considered. In this embodiment, if the bases are the same, a value of 0 is assigned, and for the purinuppurine, pyrimidine＼pyrimidine configuration, that is, 1 is specified if the bases are different but the ring size does not change, For midines and pyrimidinesuppurines, 2 is specified when the ring size of the base changes. Thus, ω ₀ reflects not only the difference in base identity, but also the degree of difference between the chemical structures of purine and pyrimidine.

The first embodiment is illustrated in Table 1:

S T s / t A G C T A 0 One 2 2 G One 0 2 2 C 2 2 0 One T 2 2 One 0

The second embodiment of the present invention further contemplates the number of elements in base s _i that are not present in base t _i at each position of the molecular structure. Purinovpurin measurements compare both large and small rings. This is the case when the molecular arrangements are most similar and the two purine molecules behave similarly in terms of the size and arrangement of their chemical elements. The measurement, referred to herein as ω _0, is calculated in one embodiment by counting the number of atoms present in the first sequence and not in the second sequence. For example, if the first sequence s has a guanine ("G") nucleotide at position i and the second sequence t has an adenine ("A") nucleotide at the corresponding position, the ω ₀ measure at position i (s here _i / t _i ) is calculated by determining the number of atoms in s _i not present in t _i and / or present in different positions. Referring now to FIG. 33, in the small ring opposite the oxygen atom (1), hydrogen atom (2) and NH ₂ groups (3, 4, 5) of the atom and the double bonded carbon atom in the guanine molecule The hydrogen (6) and carbon (7) atoms are not present in the adenine molecule or are in different positions. Thus, s _i / t _i = 7 where s _i = G and t _i = A. Thus, ω ₀ reflects the degree of difference and similarity in the chemical structure of the purine. Such differences and similarities are assumed to have biological significance in the coding and non-coding regions of nucleotide sequences. The calculation of ω ₀ may be modified using more accurate information at the level of binding to each chemical element in other embodiments.

In the calculation of omega measures, when the omega measures are equally zero, the chemistry is equally the same. If the omega measure is not equally zero, the omega measure provides a number representing the number of different chemical elements. The complete analysis for the four nucleotides is shown in Table 2 below. In the pyrimidinepyrimidine assays the s _i / t _i values are performed in a similar manner to the Purinjanpurin measure, except that only a single ring is considered. In purin＼pyrimidine or pyrimidine＼purin measurements, the large ring of purine is compared to the ring of pyrimidine, but the comparison is performed counterclockwise on the large ring of purine and clockwise (or vice versa) on the pyrimidine ring. do. The structure of the molecule is shown in FIG. However, since the structure of the nucleotide component structure changes with respect to two rings to one ring, the measure value does not change.

Using a second embodiment of the invention, a matrix can be generated to determine the value of s _i / t _i as shown in Table 2:

S T s / t A G C T A 0 7 4 9 G 6 0 7 7 C 6 10 0 6 T 9 8 4 0

34A-34C display results of omega coefficients and some examples of chemical elements included. The figure demonstrates graphically why A / G is more similar than A / C and A / T, G / A is more similar than G / C and G / T, and so forth. Omega measures produce numbers for the same G / A and G / T, but the chemical elements involved are different. The excess of the elements of the table is evident by the figure showing the contained elements. The significance in the real world of such similarities or differences is to explain how similar or different sets of sequences are in the sequence alignment search of the present invention without losing the integrity of traditional biological validity. Different difference matrices can be used based on different chemical comparisons between bases.

In this specification, those skilled in the art will be able to prepare corresponding tables for RNA and proteins.

In one embodiment, two alternative sequences t and r are compared to native sequence s:

t = AAGCC

r = AAGAC

s = ATAGC

r and t are observed that the three bases differ from s. However, r and s are not the same and the question considered is which of r and t is more similar to s.

(여기서, μ는 상수임)인 일반적인 BLAST 시스템을 사용하여, s 및 t에 대한 유사성 스코어는 다음과 같다:Using traditional methods, one can define amounts S (s, t) and S (s, r) to compare t and r to s, respectively. S (x _i , y _j ) = s (x _i , y _j ) = {+1, x _i = y _j ; -μ, x _i ≠ y _j and

Using the general BLAST system, where μ is a constant, the similarity scores for s and t are:

S (s, t) = 2-3 μ

S (s, r) = 2-3 μ

No obvious difference is observed.

Using the first embodiment of the invention as described above in connection with Table 1, the values of ω ₀ (s, r) and ω ₀ (s, t) are determined as follows:

ω ₀ (s, r) = (0 + 2 + 1 + 1 + 0) = 4

ω ₀ (s, t) = (0 + 2 + 1 + 2 + 0) = 5.

Thus, the inventors believe that a difference exists.

Using the second embodiment of the present invention as described above, the values of ω ₀ (s, r) and ω ₀ (s, t) are obtained from the following formula (1), where N is the Shorter sequence length) is determined as follows:

(1)

(One)

ω ₀ (s, r) = (0 + 9 + 6 + 7 + 0) / 80 = 22/80 = 0.275

ω ₀ (s, t) = (0 + 9 + 6 + 10 + 0) / 80 = 25/80 = 0.3125

Segment r is more similar to s than t.

Because of the excess of integers in the second embodiment, it is possible to keep up with the chemistry involved in the coefficients but to catch up with sequences that have the same values, for example A / G vs. A / C. This is an indication of how the molecules communicate differently and do not convey the same information.

For the sequence of the whole genome, standardization techniques are used and are shown in equation (2) below. Thus, in the DNA sequence, each position of the nucleotides represents a unique address in the string. In short strands, a denominator is used to measure the strength of the difference. For longer strands, the standardization technique discussed below in connection with equation (2) that eliminates exponential growth of the denominator is used. This allows VaSSA to plot each location with respect to its unique address. The omega measure of this unique position creates a unique structural behavior for each nucleotide as well as the way in which it is profiled with respect to the strands in which it is present.

Computer program products

In an exemplary embodiment, the method of the present invention may be implemented on a machine-readable medium that, when read by a machine, causes a machine, eg, a computer, to execute the method described above. In addition, this embodiment of the present invention may provide a graphical user interface (GUI) that allows a user to compare sequences of genetic material and further analyze sequences and remarks results.

For example, as shown in FIG. 3, the GUI may provide modules for file management, reporting, analysis, plotting, user option setting, and user assistance.

As shown in FIG. 4, the file management module 300 may further include a module for loading a sequence capable of loading one or more sequence files. The file may comprise a single sequence or multiple sequences. The sequence can be read from discs, CDs and the like. The sequence does not have to be stored and can be analyzed "on the fly" when received. The sequence file may be FASTA formatted or in any other format. When loaded, each sequence can be assigned a unique reference number and reviewed to ensure that all letters are valid.

File management module 300 may also include a module for flushing the active sequence, which may remove or “flush” the active sequence file from memory. When flushed, reference numbers to sequences are preserved. File management module 300 may also include a module for flushing the loaded sequence from memory. The active sequence is the sequence for which analysis is to be performed, while the loaded sequence is also a sequence in memory but not currently analyzed for it.

The module for loading the sequence can include a module for displaying the loaded sequence, which can generate and display a summary report notebook page when the sequence is loaded. As shown in FIG. 5, the summary report notebook page may display the sequence file name and number of sequences.

Report module 500 includes a sequence summary of all loaded sequences including unique reference numbers, sequence headers, and sequence lengths (FIG. 6); A list of the contents of each loaded sequence, including sequence content in a unique reference number and FASTA format (FIG. 7); And / or statistical information (FIG. 8) about each loaded sequence, including unique reference numbers, sequence headers, and coefficients of each standard sequence letter can be generated and displayed. If the sequence letter is not recognized, the reporting module generates an error signal listed in the "Error" column in the statistical information about each loaded sequence (Figure 8).

Analysis module 200 may include many submodules. For example, the sequence alignment submodule may align the target sequence to the base sequence and display the alignment report (FIG. 9). The sequence alignment module may also reverse the base sequence, reverse the mode, align the base and target to the shortest length, calculate the alignment ratio, or calculate the omega similarity score (FIG. 10). Omega similarity scores can be used to determine whether and how similar the target is to base. If the omega similarity score value is less than 1/2 ⁿ (where n is the maximum length of two sequences s and t), the two sequences can be said to be similar. If the omega similarity score exceeds 1/2 ⁿ , the sequences are said to be dissimilar.

Tasks of the menu options in the VaSSA analysis menu include, but are not limited to:

1. Reverse the base

There is an inverted base option under VaSSA's analysis menu. One function of the inverting base is to allow the user to switch sequences. For example, if the sequence is in the 5 'to 3' direction, the reverse base function is read in the 3 'to 5' direction (but not in the complementary strand direction).

2. Invert mode

The function of the mode inversion option is to invert the mode calculation. "Inverting the mode calculation" means changing s _i / t _i to t _i / s _i . This is important because by definition ω ₀ is not a symmetric operation.

3. Align the base and target sequences to the shortest length

Base and target are two sequence strings of different lengths or of the same length. If the strings are of different lengths, the first part of the analysis is aligned and stopped at the end of the shortest sequence. If they are the same length, sequencing is performed at the end of each string.

4. Calculate alpha number sort ratio and omega similarity score

Alpha number alignment is an alignment that provides a ratio that is the total number of nucleotides aligned over the total number of nucleotides. 13, Omega sub-zero (ω ₀₎ module may calculate the score for the sequence ω ₀ ω ₀ and displaying the score. One base or all loaded sequences can be selected. Reports can be sorted by reference number, length, or omega score (FIG. 14). The base sequence and mode can each be reversed.

The ω ₀ value can also be calculated by the single stranded module for the base sequence and the target sequence. Consider the following single-stranded equation, a simplified version of Equation 6 (multiple strand forms of the equation will be discussed below):

(2)

(2)

In the formula, z ₁ represents a single strand. That is, z _l = s ₀ s ₁ ..s _k ... where each s _k is A, G, C or T.

z _l ^λi corresponds to a nucleotide in the second position λ _i and λ _{i + 1-th} position (where, i is the number in the index set (index set) l = 1,2,3, ...).

Coefficient c _{λ i} = s _i / s _{i +1} λ i th position and i + 1 th position, where i is a number in index set l = 1,2,3, ...

Thus, for the exemplary four nucleotide strand z _l = ACGT, C _l (z _l ) is an array of coefficients [c ₀ , C ₁ , C ₂ ], where each coefficient is z _l for position i in the strand calculated by determining ^λi / z _l ^{λi + 1} (except for the last position), which in this case is equal to [A / C, C / G, G / T] = [6,7,8] . The coefficients can be used to form a single strand plot for strand z _l , where the position within the strand (ie the value of l) is plotted on the x-axis and the value of the corresponding coefficient is plotted on the y-axis (An example of a single strand plot for two strands is shown in FIG. 27).

The question repeat module can locate multiple occurrences of a user-specific target sequence within the base sequence and display the multiple occurrences. Multiple occurrences of the target sequence are referred to herein as repeats. VaSSA has two kinds of repeats: repeat and omega repeat. Repeat uses only the shift function for symbols, and Omega Repeat uses the shift function for measurements of omega similarity. As shown in FIG. 11, a user can select a base sequence for searching and a target sequence for searching. The user can specify thresholds to mitigate or enhance the investigation. Base or target sequences may also be reversed. The question repeat module can then generate a sub-target when the user specifies a threshold and identify the location in the base where the target or sub-target appears. In one embodiment, if the target is AGCT, the question repeat module may generate sub-targets of AGC and GCT. As shown in FIG. 12, the repeat target and subtarget are identified along with the number of times the repeat target and subtarget are detected at the top of the GUI window page. The appearance of the target sequence is identified by hat symbol 1201 and the appearance of the sub-target sequence is identified by asterisk symbol 1202.

As shown in Figures 15 and 16, the question omega repeat module obtains everything mentioned above with respect to the question repeat module. However, the module also discovers how repeated nucleotides in segments of the string can communicate differently (at least in terms of omega measures) in another segment of the string. Thus, the question omega repeat can find out when the repeat is a duplicate and when it is not.

As shown in FIGS. 17 and 18, the slope calculation module can calculate the slope for each nucleotide position in the base sequence and display the slope report. In an exemplary embodiment, the slope can be calculated using the following formula:

Ω _k = s _k / s _{k +1} -s _{k -1} / s _k (3)

In the formula, k represents the unique position of the nucleotide in the DNA sequence. ω _k = s _k / s _{k + 1} (ω _k is the kth term in the ω ₀ series). The equation can be used to generate information on curvature in a 2-D profile. When Ω _k is positive, the information conveyed increases, and the bond connecting the double strands is longer (and therefore tends to be weaker than the shorter one). When Ω _k is negative, the information conveyed decreases, and the coupling connecting the double helix is shorter (they tend to be stronger). Thus, within the plot of positive and negative, there is a profile of information flow from one position to the next in the sequence. The slope graph is a plot of the change information flow. This shows where the change in information is the same (zero in the sign chart) and different in the sequence. It also shows the case where the information is exactly the same but in the opposite direction. To generate the graph, the position of the nucleotides (shown in FIG. 30 for example) is plotted against the value of the slope. Thus, Equation 3 is to generate a slope plot in the sine chart and VaSSA. In both cases, the nucleotide specific position in the strand corresponds to the x axis and the value of Ω _k corresponds to the y axis.

In one embodiment, in the sequence AGC, the change from A to G will be calculated as follows: A is at position k-1, G is at k, and C is at k + 1. Thus, the omega (k) based on the values in Table 2 is G / C-A / G = 10-6 = 4. Thus, the change from A to G is positive and can be indicated with "+" in the slope report.

As shown in Figures 19 and 20, the sequence comparison submodule can compare target sequences to base sequences and display similarity reports. The sequence comparison submodule also reverses the base sequence, reverses the target sequence, reverses the mode, calculates the ω _n values for each base and target sequence, converts the base and target sequence to binary, The distance between the sequence and the target sequence can be calculated and determine if the distance exceeds the boundary,

As shown in FIGS. 21-25, the plot module may include many plotting submodules. For example, the spectral array submodule can plot alignment coefficients against base sequences and target sequences. The spectral array submodule can also calculate ω _n values for radial comparison and extract the alignment coefficients. In the radial comparison, the spectral array submodule can use equation (4):

(4)

(4)

In the formula,

(5)

(5)

The formula is for multiple sequences. This allows the generation of unique spectral analysis and is a notation used for multiple sums with respect to l. These are the coefficients generated within each sequence with respect to ω ₀ for their position. The nucleotide at each position of the sequence is represented by Z ₁ ^λl Z ₂ ··· Z _n ^{λ λ2l nl.}

The formation of equations 4 and 5 allows the generation of plots in VaSSA. The coefficient structure of the equation may be captured by the triangular structure shown in FIG. 25. The spectral structure is triangular and allows observing optimization without inserting or deleting spaces in the strands of DNA. 24 demonstrates how coefficients are generated when a formula is used using two strands. Single stranded plots have the same structure but different values. Because of the non-binary measures, where the plots are equal and where the different ones can be observed accurately. Where periodicity can also be observed. Since the function is analytical, it can be formulated as a shift without affecting the specificity of the nucleotide position. One embodiment is shown in FIG. 27. The spectral array plot in VaSSA uses the coefficient just below the center of the triangular structure on FIG. 25. An example of the plot is FIG. 22. It has information where they have a direct alignment because the graph is zero there. There is also a spike with a certain height. Similar information can be observed as a single stranded plot. However, the magnitude of the difference can be visualized here with respect to the height of the spikes. In addition, using a pointer in the triangle, we can complete the phase description, which is a different way than completing the optimization.

As shown in FIGS. 26-28, the single stranded submodule can plot the single strand against the base sequence and the target sequence. Single stranded submodules may also calculate ω _n values for base and target sequences. Single stranded submodules can be plotted using equation (4).

(6)

(6)

Here, equation (6) is a simplified version of equation (5). However, the equation allows to profile a single strand.

As shown in FIGS. 29-30, the slope module can calculate the slope for each nucleotide position within the base sequence and display a plot of the slope. The ω _n module can calculate ω _n for the base sequence and display a plot of ω _n . The ω _n module can use equation (6).

Generating a plot of slope will produce a plot on FIG. 30. The slope plot is a graph of the monotony of the information flow. The plot allows the user to determine local and global maximum and minimum positions on a single stranded plot. This also allows the user to determine the concavity in the local and global regions of the single stranded plot.

While various embodiments of the invention have been described above, it should be understood that they have been presented by way of example only, and not limitation. Accordingly, the breadth and scope of the present invention should not be limited by any of the above-described exemplary embodiments, but should instead be defined in accordance with the following claims and their equivalents.

                               SEQUENCE LISTING <110> CLARK, JEFFREY M. <120> SYSTEM, METHOD AND COMPUTER PROGRAM FOR NON-BINARY       SEQUENCE COMPARISON <130> 5442-001 <140> 11 / 378,284 <141> 2006-03-20 <150> 60 / 662,943 <151> 2005-03-18 <160> 8 <170> Patent In Ver. 3.3 <210> 1 <211> 12 <212> DNA <213> Artificial Sequence <220> <223> Description of Artificial Sequence: Synthetic       oligonucleotide <400> 1 agcaatatag ga 12 <210> 2 <211> 288 <212> DNA <213> Homo sapiens <400> 2 ggccgggcgc ggtggctcac gcctgtaatc ccagcacttt gggaggccga ggcgggcgga 60 tcacgaggtc aggagatcga gaccatcctg gctaacacgg tgaaaccccg tctctactaa 120 aaatacaaaa attagccggg cgtggtggcg ggcgcctgta gtcccagcta ctcgggaggc 180 tgaggcagga gaatggcgtg aacccgggag gcggagcttg cagtgagccg agatcgcgcc 240 actgcactcc agcctgggcg acagagcgag actccgtctc aaaaaaaa 288 <210> 3 <211> 290 <212> DNA <213> Homo sapiens <400> 3 ggccgggcgc ggtggctcac gcctgtaatc ccagcacttt gggaggccga ggcgggcgga 60 tcacctgagg tcaggagttc gagaccagcc tggccaacat ggtgaaaccc cgtctctact 120 aaaaatacaa aaattagccg ggcgtggtgg cgcgcgcctg taatcccagc tactcgggag 180 gctgaggcag gagaatcgct tgaacccggg aggcggaggt tgcagtgagc cgagatcgcg 240 ccactgcact ccagcctggg cgacagagcg agactccgtc tcaaaaaaaa 290 <210> 4 <211> 291 <212> DNA <213> Homo sapiens <400> 4 ggccgggcgc ggtggctcac gcctgtaatc ccagcacttt gggaggccga ggcgggcgga 60 tcacctgagg tcgggagttc gagaccagcc tgaccaacat ggagaaaccc cgtctctact 120 aaaaatacaa aaattagccg ggcgtggtgg cgcatgcctg taatcccagc tactcgggag 180 gctgaggcag gagaatcgct tgaacccggg aggcggaggt tgcggtgagc cgagatcgcg 240 ccattgcact ccagcctggg caacaagagc gaaactccgt ctcaaaaaaa a 291 <210> 5 <211> 288 <212> DNA <213> Homo sapiens <400> 5 ggccgggcgc ggtggctcac gcctgtaatc ccagcacttt gggaggccga ggcgggcgga 60 tcacgaggtc aggagatcga gaccatcccg gctaaaacgg tgaaaccccg tctctactaa 120 aaatacaaaa attagccggg cgtagtggcg ggcgcctgta gtcccagcta cttgggaggc 180 tgaggcagga gaatggcgtg aacccgggag gcggagcttg cagtgagccg agatcccgcc 240 actgcactcc agcctgggcg acagagcgag actccgtctc aaaaaaaa 288 <210> 6 <211> 288 <212> DNA <213> Homo sapiens <400> 6 ggccgggcac ggtcgtcacg cccgtcatcc cagcacttcg agaggccgag gtgggcgcat 60 cacaggaggt cgggagttgg agaccagcct gagcaacatg gagagacacg gcgtgcctac 120 taaaaacaca aacatcagcc aagccaggcg tgggggtgcc tccctgtcaa tccccgctaa 180 tcgggaggct ggaggcagga gaagcgctcg aacccgggag gcggaaggtg cggtgagcca 240 agatcgcgcc attgcactct agccgtggaa acaagagtga aactctgt 288 <210> 7 <211> 360 <212> DNA <213> Homo sapiens <400> 7 ttctttcatg gggaagcaga tttgggtacc acccaagtat tgactcaccc atcaacaacc 60 gctatgtatt tcgtacatta ctgccagcca ccatgaatat tgtacagtac cataaatact 120 tgaccacctg tagtacataa aaacccaatc cacatcaaaa ccctcccctc atgcttacaa 180 gcaagtacag caatcaacct tcaactatca cacatcaact gcaactccaa agccacctct 240 cccccactag gataccaaca aagctaccca tccttaacag tacatagcac ataaagccat 300 ttaccgtaca tagcacatta cagtcaaatc ccttcctgtc cccatggatg acccccctca 360 <210> 8 <211> 360 <212> DNA <213> Homo sapiens <400> 8 ttctttcatg gggaagcaga tttgggtacc acccaagtat tgactcaccc atcaacaacc 60 gctatgtatt tcgtacatta ctgccagcca ccatgaatat tgtacggtac cataaatact 120 tgaccacctg tagtacataa aaacccaacc cacatcaaac cccccccccc atgcttacaa 180 gcaagtacag caatcaacct tcaactatca cacatcaact gcaactccaa agccacccct 240 cacccactag gataccaaca aacctaccca cccttaacag tacatagtac ataaagccat 300 ttaccgtaca tagcacatta cagtcaaatc ccttctcgtc cccatggatg acccccctca 360  

Claims (38)

An analysis module adapted to calculate a non-binary similarity score between the first nucleotide sequence and the second nucleotide sequence; And Outputs in communication with the analysis module for outputting similarity scores Including a system for sequence analysis. The system of claim 1, wherein the similarity score is based on a combination of similarity scores for each base pair. The system of claim 2, wherein the similarity score for the base pairs is dependent on the similarity of the chemical structure of the base pairs. 4. The method of claim 3, wherein the similarity score for the base pairs is a first value if the nucleotides of the base pairs match, a second value if the nucleotides of the base pairs do not match but have the same structure, and the first, second and third values Is different. The system of claim 3, wherein the similarity score for the base pairs is determined based on the relative position of the base pairs. The system of claim 3, wherein the similarity score for the base pairs is based on the number of elements in the nucleotides of the first sequence that are not present in the nucleotides of the second sequence. The system of claim 1 further comprising a report module, a file management module, and a plot module. 8. The system of claim 7, further comprising a user option module or a user help module, or both. The method of claim 1, wherein the file management module A sequence load module adapted to load one or more sequence files; An active sequence flush module adapted to flush the sequence file from memory; And Loaded sequence flush module adopted to flush a loaded sequence file from the memory System comprising a. 10. The system of claim 9, wherein the sequence load module comprises a loaded sequence display module adapted to generate and display a summary report notebook page when a sequence is loaded, wherein the summary report notebook page is a sequence file name and The system being adapted to display the number of sequences. The system of claim 1, wherein the report module is adapted to generate and display one or more of a sequence summary, a list of the content of each loaded sequence, or statistical information for each loaded sequence. . The method of claim 1, wherein the analysis module A sequence alignment module adapted to align the target sequence to the base sequence and display an alignment report; A ω ₀ module adapted to calculate a ω ₀ score for the sequence and display the ω ₀ score; A query repeat module adapted to locate multiple occurrences of the target sequence within the base sequence and display the multiple occurrences; A question omega repeat module adopted to determine when the repeated nucleotide is a duplicate; A gradient calculation module adapted to calculate a slope for each nucleotide position within the base sequence and display a slope report; And A sequence comparison module adapted to compare the target sequence with the base sequence and display a similarity report. The method of claim 12, wherein the sequence alignment module reverses the base sequence, reverses the mod, aligns the base and the target to the shortest length, calculates an alignment ratio, or scores an omega similarity The system is further adopted to perform one or more of the calculations. The method of claim 12, wherein the sequence comparison module reverses the base sequence; Inverting the target sequence; Reverse mode; The system is further adapted to perform one or more of calculating a ω ₀ value for each said base and said target sequence. The method of claim 1, wherein the plot module A spectral array module adapted to plot alignment coefficients against base and target sequences; A single stranded module adapted to plot a single strand against the base sequence and the target sequence; A tilt module adapted to calculate a slope for each nucleotide position within the base sequence and display a plot of the slope, and The system calculates the ω _N for the nucleotide sequence and comprises a ω _N module adapted to display a plot of the ω _N. The system of claim 15, wherein the spectral array module is further employed to calculate ω _N values for radial comparison and extract alignment coefficients. The system of claim 15, wherein the single stranded module is further adapted to calculate ω _N values for the base sequence and the target sequence. The system of claim 1, wherein the analysis module comprises a single stranded DNA analysis module and a multiple stranded DNA analysis module. 19. The method of claim 18, wherein each of the single stranded DNA analysis module and the multi-stranded DNA analysis module are DNA approximation module, chaotic region classification module, DNA induction module, DNA branching module, DNA orbit module, analytical behavior profiler. And at least one module selected from the group consisting of a module, a DNA topological conjugacy module, a structural stable region module, a non-degradable region module, a DNA complexity base module, and a DNA aligner module. 20. The DNA approximation module of claim 19, wherein the DNA approximation module is a holomorphic shape generator module, an approximation constructor module, a P & Q coefficient calculator module, a JC-DNA curve generator module, a low complexity generator module, a target classifier module, a symbolic DNA. And at least one module selected from the group consisting of an orbit module and an analytical DNA orbit module. The system of claim 19, wherein the chaotic region classification module further comprises one or more modules selected from the group consisting of a DNA sensitivity generator module, a DNA transition generator module, and a dense periodic sequence generator module. 20. The module of claim 19, wherein the DNA induction module further comprises one or more modules selected from the group consisting of an induction generator module and a monotonic generator module, wherein the forge generator module is a positive measure module and a negative. And a measure module. 20. The system of claim 19, wherein said DNA branch module further comprises one or more modules selected from the group consisting of a DNA transitivity splitter profiler module and a DNA dense splitter profiler module. The system of claim 19, wherein the DNA orbit module further comprises one or more modules selected from the group consisting of symbolic DNA orbit modules and analytical DNA orbit modules. 25. The apparatus of claim 24, wherein the symbolic DNA orbit module comprises a symbolic flow generator module, a row difference generator module, and an orbit generator module, wherein the analytical DNA orbit module is an analytical forward profiler module, an analytical backward profiler module. A DNA retractor generator module, and a DNA retractor generator module. 20. The method of claim 19, wherein the analytical behavior profiler module further comprises at least one module selected from the group consisting of an algebraic structure generator module, a homomorphism-generator module, and an isomorphism-generator module. It is to include a system. 20. The method of claim 19, wherein the DNA phase conjugate module is selected from the group consisting of an analytical profiler module, an analytic mapper module, a conjugate comparison module, a first iterative analysis module, and a phase portrait generator module. The system further comprises the above module. 20. The system of claim 19, wherein the structural stable region module further comprises one or more modules selected from the group consisting of a repeat generator module, an forward asymptotic module, and a stability profiler module. The system of claim 19, wherein the non-degradable region module further comprises one or more modules selected from the group consisting of a DNA orbit analysis module, a non-repeat generator module, and a non-degradable profiler module. 20. The system of claim 19, wherein said DNA complexity base module further comprises one or more modules selected from the group consisting of a repeat generator module, a universal DNA based generator module, and a density generator module. The system of claim 19, wherein the DNA aligner module further comprises one or more modules selected from the group consisting of a symbol aligner module and an omega compare aligner module. Reading the sequence file; Selecting a target sequence and a base sequence from the file; Performing a non-membered comparison between the target and each base pair of base sequences to generate a comparison for each base pair; And Determining similarity between the target and the base sequence based on the comparison value Sequence analysis method comprising a. 33. The method of claim 32, further comprising recording the aligned sequence in the file, and calculating the alignment ratio. 33. The method of claim 32, further comprising generating one or more of a two-dimensional spectral array plot or a two-dimensional single strand plot. 35. The method of claim 34, wherein generating the spectral array plot includes calculating ω _N , performing a radial comparison, extracting alignment coefficients, and plotting the alignment coefficients. Way. The method of claim 35, further comprising reversing one of the base or the target, and reversing the calculation. 33. The method of claim 32, wherein performing the non-binary comparison uses a look-up table that includes non-binary similarity score values for a plurality of possible comparisons between two sequence components. And a step. 33. The method of claim 32, wherein similarity is

Determined by.

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